Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This application claims priority to U.S. Provisional Patent Application No. 63/658,612, filed 06/11/2024, entitled “Techniques for Unstructured Data Quality Monitoring”, U.S. Provisional Patent Application No. 63/671,957, filed 07/16/2024, entitled “Techniques for Unstructured Data Quality Monitoring”, and U.S. Provisional Patent Application No. 63/801,419, filed 05/07/2025.
This communication is responsive to amendment filed on 11/11/2025.
Status of claims:
Claims 1, 17 and 24 are amended.
Claims 1-30 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/17/2025, 11/11/2025, and 12/17/2025 comply with the provisions of M.P.E.P 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
Response to Arguments
Applicant made the following arguments:
Regarding the 101 rejections: Applicant argued that "one or more processors" have
physical or tangible form, and that the rejection is improper because a software per se rejection only applies if the claims are directed to “products that do not have a physical or tangible form and therefore claim 17 also include "one or more processors" and have physical or tangible form. In response, Examiner disagree. Claim 17 is a system comprises "one or more processors" and “a computer-readable medium”. No wherein the specification that indicate/define the processors" and “computer-readable medium” is a hardware device. Hence, all of the elements would reasonably be interpreted by one of ordinary skill in light of the disclosure as software, such the system is software, per se. Therefore, renders the system at most software per se, failing to fall within a statutory category. Accordingly, the claim lacks the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101 (See MPEP § 2106.01). Therefore, the rejection is maintained.
Regarding the 103 (a) rejections: Applicants' arguments with respect to the
amended and newly added limitations have been considered in analyzing of the rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 17 is direct to “a system comprising: one or more processors; and a computer-readable medium". The specification does not define the processors/computer-readable medium is a hardware device. The claim does not positive recited a memory. It is at best, for use with the system claims, where all of the elements would reasonably be interpreted by one of ordinary skill in light of the disclosure as software, such the system is software, per se. Therefore, renders the system at most software per se, failing to fall within a statutory category. Thus, in order to overcome this 35 USC § 101 rejection the claim need to be amended to include physical computer hardware (i.e. a memory, a computer) to execute the software components. See MPEP § 2106.01
Accordingly, the claim lack the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101. They are clearly not a series of steps or acts to be a process nor are they a combination of chemical compounds to be a composition of matter. As such, they fail to fall within a statutory category. They are, at best, functional descriptive material per se.
- Claims 18-23 are dependent on claim 17. Therefore, they are rejected under the same rational as claim 17 above.
Claims 1-16 and 24-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1 and 24 are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One:
The claims recite the following limitations directed to an abstract idea:
“identifying….; identifying…; identifying…; causing…to analyze…” are processes that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the limitations “identifying….; identifying…; identifying…; causing…to analyze…””, in the context of the claim encompasses one can manually or mentally with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas.
Step 2A, Prong Two:
This judicial exception is not integrated into a practical application. The claim recites the additional element:
“extracting…; causing…to perform…; causing…to perform…; causing…to perform…; causing…to perform…;receiving…; outputting…”. which represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g).
At Step 2B:
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (i.e. ML) as a tool are carried over and do not provide significantly more.
The claims recite “extracting…; causing…to perform…; causing…to perform…; causing…to perform…; causing…to perform…;receiving…; outputting…”. These are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
“a computer-readable medium; processor(s)”, which are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Looking at the claims as a whole does not change this conclusion and the claims appears to be ineligible
As per claims 2-5, 10 and 13-14, the claim recites the limitation(s), which are under its roadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers
performance of the limitation in the mind but for the recitation of generic computer
components, then it falls within the "Mental Processes" grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
As per claims 6-9, 11-12, 15-16, 25-30, the claims recite the limitation(s), which represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g)). These are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out the invention.
Claims 1-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 17, and 24: recite the newly added terms “a data-quality analysis” and/ “a data-quality criteria”, which are not mention in the specification. Appropriate correction is required.
All dependent claims are rejected for the same reasons given in their respective parent claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over Alperin (US 12/073,930), filed date of 12/12/2023, hereinafter “Alperin”, in view of Tsuzuku et al., (US 2021/0157856), publication date 2021-05-27, hereinafter “Tsuzuku”, and Kislal et al., (US 2024/0128420, hereinafter “Kislal”, publication date 2024-01-11.
As per claim 1, Alperin discloses a method performed by a computing system, the method comprising:
- identifying a plurality of documents that include unstructured data (col.15, lines 55-col.16, line 2, identifying a massive datasets, which a large language model used for the training data, wherein the training sets include a variety of subject matters, such as, medical report documents, electronic health records, entity documents, business documents, inventory documentation, emails, user communications, advertising documents, newspaper articles, and the like);
- identifying a prompt defining a data-quality analysis for a machine learning (ML) model, the prompt indicating instructions (col. 8 line 61 to col. 9 line 15, using the gramma rule to help identifying words in document, wherein a lexicon include a list or set of words that are allowed to occur in document; col.13, lines 25-46, inquiry machine learning model is generally trained using a non-user specific training data wherein “non-user specific training data” is training data comprised of a large and diverse dataset that does not contain data that is specific to the user; col. 20 lines 43-60, machine learning algorithms can identify patterns, correlations, and dependencies that contribute to generating the inquiry machine learning model, wherein these algorithms can extract valuable insights from various sources, including text, document; and col.15, line 55- col.16 line 2, identifying a massive datasets, which a large language model used for the training data, wherein the training sets include a variety of subject matters, such as, medical report documents, newspaper articles, and the like) to:
extract, from the plurality of documents, a set of characteristic attributes defined in the prompt (col. 20 lines 43-60, machine learning algorithms can identify patterns, correlations, and dependencies that contribute to generating the inquiry machine learning model, wherein these algorithms can extract valuable insights from various sources, including text, document; and col.6, lines 2-25, col.10 lines 4-35; col.11 lines 30-52, user is prompted to input specific information and extracting specific health-related parameters or measurements, such as heart rate, blood pressure, or chemical markers and utilizing the extracted metadata and engineered features, the processor may perform various analyses, such as statistical analysis, machine learning modeling), and
identify, based on the prompt, a set of issue attributes corresponding to data-quality criteria indicated by the prompt (col.6, lines 2-25 and col.10, lines 4-35 and col.11, line 55-col.12, line 47, user is prompted to input specific information and check for any missing or erroneous information, and correct or flag such issues).
However, Alperin does not explicitly disclose the claimed “analyzing the unstructured data in the plurality of documents and causing the ML model to analyze the unstructured data in the plurality of documents according to the prompt.
Meanwhile, Tsuzuku discloses analyzing the unstructured data in the plurality of documents (par. [0001] and [0028], analyzing the unstructured data)
- causing the ML model to analyze the unstructured data in the plurality of documents according to the prompt (par. [0001] and [0028], analyzes the unstructured text data of a whole document set, and extract facets based on the machine learning);
causing the ML model to perform a first pass that includes individually analyzing documents of the plurality of documents to generate first output that includes, for each respective document, extracted characteristic attributes and identified issue attributes of the respective document (par. [0028], making a first-pass search of a given query string or search context, a similar document ranking system or technology is utilized);
causing the ML model to perform a second pass that includes analyzing clusters of documents clustered by characteristic attributes of the first output, wherein analyzing the clusters of documents includes, for each cluster of documents, analyzing the characteristic attributes of the first output for documents in the cluster to generate second output that includes cluster-level characteristic attributes identified according to the set of characteristic attributes defined in the prompt (par. [0031], perform clustering against similar documents in a document subset, making a “diff’, wherein the context of the search will not be well incorporated, the same result can happen in extracting positive facets, but is more noticeable in extracting negative facets; and par. [0029], assumes that the facilities have performed a first-pass search result determination and a second-pass re-ranking of the search results to identify and rank the most applicable documents to the search context); and
causing the ML model to perform a third pass that includes analyzing the cluster-level characteristic attributes of the second output to generate third output that includes one or more collection-level characteristic attributes identified according to the set of characteristic attributes defined in the prompt (par. [0024], grouping into a document subset all of the resulting documents that fit a query, counting the facet values associated with the document subset, and computing frequency, correlation, and/or a timeline analysis (asexamples) for the document subset. In document subset refinement, consideration of the facet aggregation can lead to an extraction of a refined document subset. As a result of repeated aggregation and refinement, knowledge can be obtained and new findings discovered; and par. [0031], perform clustering against similar documents in a document subset, making a “diff”, wherein the context of the search will not be well incorporated, the same result can happen in extracting positive facets, but is more noticeable in extracting negative facets; par. [0031], perform clustering against similar documents in a document subset, making a “diff’, wherein the context of the search will not be well incorporated, the same result can happen in extracting positive facets, but is more noticeable in extracting negative facets; and par.[0028]-[0029], assumes that the facilities have performed a first-pass search result determination and a second-pass re-ranking of the search results to identify and rank the most applicable documents to the search context); and
- receiving results from the ML model in a structured format according to the prompt, the results based on the first output of the first pass, the second output of the second pass, and the third output of the third pass (par. [0024], grouping into a document subset all of the resulting documents that fit a query, counting the facet values associated with the document subset, and computing frequency, correlation, and/or a timeline analysis (as examples) for the document subset. In document subset refinement, consideration of the facet aggregation can lead to an extraction of a refined document subset. As a result of repeated aggregation and refinement, knowledge can be obtained and new findings discovered.
Therefore, it would have obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alperin to analyze the unstructured data in the plurality of documents, in order to identify positive or negative information entity associated with informational elements, in similar documents to search context.
Neither Alperin nor Tsuzuku discloses the claimed “outputting a representation of the results returned by the ML model”.
On the other hand, Kislal discloses outputting a representation of the results returned by the ML model (par. [0011], deriving the structured output information for the one or more documents may include one or more of determining classification information for the one or more documents representative of at least one of the concepts, performing data clustering for the one or more documents based on the answer data, applying a data discovery process to the answer data to determine one or more labels relevant to the one or more concepts associated with the one or more documents, and generating an output report based on the answer data, and/or deriving supplemental data relevant to at least some of the answer data).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the combined system of the cited references to include the feature as disclosed by Glover to outputting a representation of the results returned by the ML model, in order to efficiently summarize and structure of unstructured documents.
As per claim 2, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses the prompt includes instructions to return results for each characteristic attribute in the set of characteristic attributes, the results for each characteristic attribute including, for each document in the plurality of documents, one or more of the following: a category represented by a respective document and determined by the ML model, an integer representing a count associated with the respective document and determined by the ML model, or a string describing the respective document and determined by the ML model (col.40, lines 43-65).
As per claim 3, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses the prompt includes instructions to return results for each issue attribute in the set of issue attributes, the results for each issue attribute including, for each document in the plurality of documents, one or more of the following: a Boolean value representing presence of the issue attribute in a respective document and determined by the ML model, or a representation of unstructured content from the respective document that caused the issue attribute to be identified as present by the ML model (col.6, lines 2-25 and col.10, lines 4-35 and col.11, line 55-col.12, line 47, user is prompted to input specific information and check for any missing or erroneous information, and correct or flag such issues).
As per claim 4, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses the set of characteristic attributes includes one or more of the following characteristic attributes: a topic of a target set of documents, a sentiment of a target set of documents, a summary of a target set of documents, a tone of a target set of documents, a language of a target set of documents, a quality grade of a target set of documents, or a category of a target set of documents (col.14, lines 20-31, measures are taken to maintain the quality of the data, removing erroneous or misleading information).
As per claim 5, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses the set of issue attributes include one or more of the following issue attributes: presence of personally identifiable information (PII) in a document, presence of abusive language in a document, presence of sensitive information in a document, or presence of duplicate documents (col.14, lines 46-62, anonymization involves removing or obfuscating personally identifiable information (PII) and sensitive data while retaining the utility and quality of the data for model training).
As per claim 6, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses the first output of the first pass includes representations of unstructured content that caused one or more issue attributes to be identified as present by the ML model (col.6, lines 2-25 and col.10, lines 4-35 and col.11, line 55-col.12, line 47, user is prompted to input specific information and check for any missing or erroneous information, and correct or flag such issues).
As per claim 7, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses the results include a score for each document in the plurality of documents, the score determined by the ML model based on one or more of the following: one or more characteristic attributes extracted from the respective document, or presence of one or more issue attributes identified in the respective document (col.6, lines 2-25 and col.10, lines 4-35 and col.11, line 55-col.12, line 47, user is prompted to input specific information and check for any missing or erroneous information, and correct or flag such issues).
As per claim 8, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses receiving input identifying one or more attribute classes, the one or more attribute classes corresponding to one or more characteristic attributes in the set of characteristic attributes, one or more issue attributes in the set of issue attributes, or a combination of both; and
- generating the prompt including instantiating the one or more attribute classes (col.21, lines 10-21).
As per claim 9, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Tsuzuku discloses receiving input specifying the ML model to use for analyzing the unstructured data in the plurality of documents (par. [0001] and [0028], analyzes the unstructured text data of a whole document set, and extract facets based on the machine learning).
As per claim 10, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses validating the results received from the ML model including determining whether the structured format of the results complies with formatting instructions in the prompt (col.15, lines 40-52, verification process may identify low-confidence portions of the non-user specific training data by cross-reference the non-user specific training data with established databases, published literature, or official medical records to validate its accuracy).
As per claim 11, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses in response to a determination that the structured format of the results does not comply with the formatting instructions in the prompt, prompting the ML model to fix a non-compliant portion of the results (col.10, lines 6-35).
As per claim 12, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Kislal discloses outputting the representation of the results includes one or both of the following: generating one or more visualizations based on the results; or storing the representation of the results in a storage (par. [0011], deriving the structured output information for the one or more documents may include one or more of determining classification information for the one or more documents representative of at least one of the concepts, performing data clustering for the one or more documents based on the answer data, applying a data discovery process to the answer data to determine one or more labels relevant to the one or more concepts associated with the one or more documents, and generating an output report based on the answer data, and/or deriving supplemental data relevant to at least some of the answer data).
As per claim 13, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses the unstructured data includes one or more of the following: text data, audio data, or visual data (col.11, lines 10-27, unstructured data (e.g., text)) .
As per claim 14, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses modifying the plurality of documents based on the results returned by the ML model, wherein modifying the plurality of documents includes one or more of the following: removing one or more documents from the plurality of documents based on identified presence of one or more issue attributes (col.15, lines 45-54, identify low-confidence portions of the non-user specific training data by cross-reference the non-user specific training data with established databases, published literature, or official medical records to validate its accuracy. Ensure that the data aligns with existing validated information and if the low-confidence portions of the non-user specific training data are proven to be invalid by processor and/or a medical practitioner and those portions of the training data are removed); or
- redacting content within one or more documents based on presence of one or more issue attributes (col.15, lines 2-8, anonymization processes may include redacting the patient identifiers within the non-user specific training data and/or the inquiry training data, wherein redacting is done using various methods like blacking out, using placeholders, or applying software tools to mask or replace the sensitive data)
As per claim 15, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses using the modified plurality of documents to train a different ML model (col.20, lines 43-67).
As per claim 16, the combination of Alperin, Tsuzuku and Kislal discloses the invention as claimed. In addition, Alperin discloses modifying the plurality of documents includes outputting a modified set of documents that is distinct from the plurality of documents (col.20, lines 43-67).
As per claims 17-23, are the system claims, which are the same as the method claims 1-5, 10, and 14 above. Therefore, claims 17-23 are rejected under the same rational as claims 1-5, 10, and 14 above.
As per claims 24-30, are the non-transitory computer-readable medium claims, which are the same as the method claims 1-5, 10, and 14 above. Therefore, claims 24-30 are rejected under the same rational as claims 1-5, 10, and 14 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOAN T NGUYEN whose telephone number is (571)-270-3103. The examiner can normally be reached on Monday from 10:00 am - 6:00 pm, Thursday-Friday from 10:00 am - 2:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached on (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4103. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
1/26/2025
/LOAN T NGUYEN/Examiner, Art Unit 2165